Papers by Mithun Paul
Grounding Gradable Adjectives through Crowdsourcing (L18-1)
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| Challenge: | Often, texts describe interactions using vague, high-level language . crowdsourcing is expensive and requires extensive literature review and time . |
| Approach: | They propose a method for estimating concrete groundings for a set of gradable adjectives by crowdsourcing human intuitions and fitting a mixed effects model to the text. |
| Outcome: | The proposed model can generalize to unseen data and has a predictive R 2 of 0.632 in general and 0.677 on a subset of high-frequency adjectives. |
Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models (N19-4)
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Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A. Valenzuela-Escárcega, Ajay Nagesh, Vikas Yadav, John Bachman, Zheng Tang, Heather Lent, Fan Luo, Mithun Paul, Steven Bethard, Kobus Barnard, Clayton Morrison, Mihai Surdeanu
| Challenge: | a paper proposes a method for building probabilistic models of complex phenomena such as food insecurity . currently, these models are hand-built for each new situation and require months to construct . |
| Approach: | They propose an approach that builds executable probabilistic models from raw, free text. |
| Outcome: | The proposed approach builds executable probabilistic models from raw, free text. |
On the Importance of Delexicalization for Fact Verification (D19-1)
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| Challenge: | Neural networks (NNs) perform state-of-the-art (SOA) performance in many complex tasks. |
| Approach: | They investigate the importance that a model assigns to various aspects of data . they experiment with two strategies of masking to mitigate this dependence on lexicalized information . |
| Outcome: | The proposed model improves on the in-domain dataset by 10% compared to the fully lexicalized model. |